Overview

Dataset statistics

Number of variables16
Number of observations6050
Missing cells1610
Missing cells (%)1.7%
Duplicate rows50
Duplicate rows (%)0.8%
Total size in memory756.4 KiB
Average record size in memory128.0 B

Variable types

Numeric10
Categorical6

Alerts

Dataset has 50 (0.8%) duplicate rowsDuplicates
salary has 403 (6.7%) missing valuesMissing
experience_years has 403 (6.7%) missing valuesMissing
city has 404 (6.7%) missing valuesMissing
score has 400 (6.6%) missing valuesMissing
experience_years has 137 (2.3%) zerosZeros
last_promotion_years has 386 (6.4%) zerosZeros
projects_completed has 117 (1.9%) zerosZeros
overtime_hours has 182 (3.0%) zerosZeros
leaves_taken has 148 (2.4%) zerosZeros

Reproduction

Analysis started2026-02-06 04:59:23.825751
Analysis finished2026-02-06 04:59:40.697470
Duration16.87 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct52
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.678678
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:40.809630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median44
Q356
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.913767
Coefficient of variation (CV)0.34144274
Kurtosis-1.1783138
Mean43.678678
Median Absolute Deviation (MAD)13
Skewness-0.014911139
Sum264256
Variance222.42046
MonotonicityNot monotonic
2026-02-06T10:29:40.958317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66144
 
2.4%
64138
 
2.3%
45134
 
2.2%
38133
 
2.2%
25133
 
2.2%
49132
 
2.2%
34131
 
2.2%
52130
 
2.1%
62129
 
2.1%
54128
 
2.1%
Other values (42)4718
78.0%
ValueCountFrequency (%)
18111
1.8%
19115
1.9%
20118
2.0%
21120
2.0%
22108
1.8%
23110
1.8%
2487
1.4%
25133
2.2%
26110
1.8%
27106
1.8%
ValueCountFrequency (%)
69107
1.8%
68124
2.0%
67106
1.8%
66144
2.4%
65100
1.7%
64138
2.3%
63105
1.7%
62129
2.1%
61115
1.9%
6096
1.6%

salary
Real number (ℝ)

Missing 

Distinct5489
Distinct (%)97.2%
Missing403
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean84718.989
Minimum20028
Maximum149999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:41.073381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20028
5-th percentile25624.1
Q151219
median85552
Q3118259
95-th percentile143136.8
Maximum149999
Range129971
Interquartile range (IQR)67040

Descriptive statistics

Standard deviation37851.683
Coefficient of variation (CV)0.44679101
Kurtosis-1.2372272
Mean84718.989
Median Absolute Deviation (MAD)33535
Skewness-0.017210803
Sum4.7840813 × 108
Variance1.4327499 × 109
MonotonicityNot monotonic
2026-02-06T10:29:41.215859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702483
 
< 0.1%
1228283
 
< 0.1%
639192
 
< 0.1%
1021032
 
< 0.1%
1424432
 
< 0.1%
233582
 
< 0.1%
1278942
 
< 0.1%
1414852
 
< 0.1%
1434162
 
< 0.1%
1259822
 
< 0.1%
Other values (5479)5625
93.0%
(Missing)403
 
6.7%
ValueCountFrequency (%)
200281
< 0.1%
200661
< 0.1%
200741
< 0.1%
201321
< 0.1%
201531
< 0.1%
201781
< 0.1%
202151
< 0.1%
202281
< 0.1%
202291
< 0.1%
202871
< 0.1%
ValueCountFrequency (%)
1499991
< 0.1%
1499491
< 0.1%
1499001
< 0.1%
1498631
< 0.1%
1498561
< 0.1%
1497531
< 0.1%
1497161
< 0.1%
1496881
< 0.1%
1496481
< 0.1%
1496421
< 0.1%

experience_years
Real number (ℝ)

Missing  Zeros 

Distinct40
Distinct (%)0.7%
Missing403
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean19.418806
Minimum0
Maximum39
Zeros137
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:41.391995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median19
Q329
95-th percentile37
Maximum39
Range39
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.36937
Coefficient of variation (CV)0.58548244
Kurtosis-1.1532562
Mean19.418806
Median Absolute Deviation (MAD)10
Skewness0.015011255
Sum109658
Variance129.26258
MonotonicityNot monotonic
2026-02-06T10:29:41.572524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
15169
 
2.8%
12165
 
2.7%
14160
 
2.6%
18159
 
2.6%
16157
 
2.6%
25156
 
2.6%
21152
 
2.5%
13152
 
2.5%
9149
 
2.5%
22149
 
2.5%
Other values (30)4079
67.4%
(Missing)403
 
6.7%
ValueCountFrequency (%)
0137
2.3%
1140
2.3%
2140
2.3%
3139
2.3%
4126
2.1%
5129
2.1%
6135
2.2%
7143
2.4%
8144
2.4%
9149
2.5%
ValueCountFrequency (%)
39127
2.1%
38144
2.4%
37138
2.3%
36145
2.4%
35138
2.3%
34121
2.0%
33124
2.0%
32141
2.3%
31131
2.2%
30148
2.4%

education_level
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
Master
1544 
Bachelor
1525 
High School
1512 
PhD
1469 

Length

Max length11
Median length8
Mean length7.0252893
Min length3

Characters and Unicode

Total characters42503
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowHigh School
3rd rowPhD
4th rowBachelor
5th rowPhD

Common Values

ValueCountFrequency (%)
Master1544
25.5%
Bachelor1525
25.2%
High School1512
25.0%
PhD1469
24.3%

Length

2026-02-06T10:29:41.801379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-06T10:29:41.961341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
master1544
20.4%
bachelor1525
20.2%
high1512
20.0%
school1512
20.0%
phd1469
19.4%

Most occurring characters

ValueCountFrequency (%)
h6018
14.2%
o4549
 
10.7%
e3069
 
7.2%
a3069
 
7.2%
r3069
 
7.2%
l3037
 
7.1%
c3037
 
7.1%
M1544
 
3.6%
s1544
 
3.6%
t1544
 
3.6%
Other values (8)12023
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)42503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h6018
14.2%
o4549
 
10.7%
e3069
 
7.2%
a3069
 
7.2%
r3069
 
7.2%
l3037
 
7.1%
c3037
 
7.1%
M1544
 
3.6%
s1544
 
3.6%
t1544
 
3.6%
Other values (8)12023
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h6018
14.2%
o4549
 
10.7%
e3069
 
7.2%
a3069
 
7.2%
r3069
 
7.2%
l3037
 
7.1%
c3037
 
7.1%
M1544
 
3.6%
s1544
 
3.6%
t1544
 
3.6%
Other values (8)12023
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h6018
14.2%
o4549
 
10.7%
e3069
 
7.2%
a3069
 
7.2%
r3069
 
7.2%
l3037
 
7.1%
c3037
 
7.1%
M1544
 
3.6%
s1544
 
3.6%
t1544
 
3.6%
Other values (8)12023
28.3%

department
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
Finance
1256 
Sales
1222 
HR
1214 
Marketing
1191 
IT
1167 

Length

Max length9
Median length7
Mean length5.0219835
Min length2

Characters and Unicode

Total characters30383
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowMarketing
3rd rowHR
4th rowFinance
5th rowSales

Common Values

ValueCountFrequency (%)
Finance1256
20.8%
Sales1222
20.2%
HR1214
20.1%
Marketing1191
19.7%
IT1167
19.3%

Length

2026-02-06T10:29:42.118265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-06T10:29:42.226058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
finance1256
20.8%
sales1222
20.2%
hr1214
20.1%
marketing1191
19.7%
it1167
19.3%

Most occurring characters

ValueCountFrequency (%)
n3703
 
12.2%
a3669
 
12.1%
e3669
 
12.1%
i2447
 
8.1%
F1256
 
4.1%
c1256
 
4.1%
S1222
 
4.0%
l1222
 
4.0%
s1222
 
4.0%
H1214
 
4.0%
Other values (8)9503
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n3703
 
12.2%
a3669
 
12.1%
e3669
 
12.1%
i2447
 
8.1%
F1256
 
4.1%
c1256
 
4.1%
S1222
 
4.0%
l1222
 
4.0%
s1222
 
4.0%
H1214
 
4.0%
Other values (8)9503
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n3703
 
12.2%
a3669
 
12.1%
e3669
 
12.1%
i2447
 
8.1%
F1256
 
4.1%
c1256
 
4.1%
S1222
 
4.0%
l1222
 
4.0%
s1222
 
4.0%
H1214
 
4.0%
Other values (8)9503
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n3703
 
12.2%
a3669
 
12.1%
e3669
 
12.1%
i2447
 
8.1%
F1256
 
4.1%
c1256
 
4.1%
S1222
 
4.0%
l1222
 
4.0%
s1222
 
4.0%
H1214
 
4.0%
Other values (8)9503
31.3%

city
Categorical

Missing 

Distinct5
Distinct (%)0.1%
Missing404
Missing (%)6.7%
Memory size47.4 KiB
Hyderabad
1217 
Bangalore
1142 
Delhi
1120 
Mumbai
1092 
Pune
1075 

Length

Max length9
Median length6
Mean length6.6742827
Min length4

Characters and Unicode

Total characters37683
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBangalore
2nd rowDelhi
3rd rowBangalore
4th rowDelhi
5th rowBangalore

Common Values

ValueCountFrequency (%)
Hyderabad1217
20.1%
Bangalore1142
18.9%
Delhi1120
18.5%
Mumbai1092
18.0%
Pune1075
17.8%
(Missing)404
 
6.7%

Length

2026-02-06T10:29:42.371648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-06T10:29:42.470738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hyderabad1217
21.6%
bangalore1142
20.2%
delhi1120
19.8%
mumbai1092
19.3%
pune1075
19.0%

Most occurring characters

ValueCountFrequency (%)
a5810
15.4%
e4554
12.1%
d2434
 
6.5%
r2359
 
6.3%
b2309
 
6.1%
l2262
 
6.0%
n2217
 
5.9%
i2212
 
5.9%
u2167
 
5.8%
y1217
 
3.2%
Other values (9)10142
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)37683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5810
15.4%
e4554
12.1%
d2434
 
6.5%
r2359
 
6.3%
b2309
 
6.1%
l2262
 
6.0%
n2217
 
5.9%
i2212
 
5.9%
u2167
 
5.8%
y1217
 
3.2%
Other values (9)10142
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5810
15.4%
e4554
12.1%
d2434
 
6.5%
r2359
 
6.3%
b2309
 
6.1%
l2262
 
6.0%
n2217
 
5.9%
i2212
 
5.9%
u2167
 
5.8%
y1217
 
3.2%
Other values (9)10142
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5810
15.4%
e4554
12.1%
d2434
 
6.5%
r2359
 
6.3%
b2309
 
6.1%
l2262
 
6.0%
n2217
 
5.9%
i2212
 
5.9%
u2167
 
5.8%
y1217
 
3.2%
Other values (9)10142
26.9%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
Other
2050 
Female
2025 
Male
1975 

Length

Max length6
Median length5
Mean length5.0082645
Min length4

Characters and Unicode

Total characters30300
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowMale
4th rowOther
5th rowFemale

Common Values

ValueCountFrequency (%)
Other2050
33.9%
Female2025
33.5%
Male1975
32.6%

Length

2026-02-06T10:29:42.622721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-06T10:29:42.727724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
other2050
33.9%
female2025
33.5%
male1975
32.6%

Most occurring characters

ValueCountFrequency (%)
e8075
26.7%
a4000
13.2%
l4000
13.2%
O2050
 
6.8%
t2050
 
6.8%
h2050
 
6.8%
r2050
 
6.8%
F2025
 
6.7%
m2025
 
6.7%
M1975
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)30300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8075
26.7%
a4000
13.2%
l4000
13.2%
O2050
 
6.8%
t2050
 
6.8%
h2050
 
6.8%
r2050
 
6.8%
F2025
 
6.7%
m2025
 
6.7%
M1975
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8075
26.7%
a4000
13.2%
l4000
13.2%
O2050
 
6.8%
t2050
 
6.8%
h2050
 
6.8%
r2050
 
6.8%
F2025
 
6.7%
m2025
 
6.7%
M1975
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8075
26.7%
a4000
13.2%
l4000
13.2%
O2050
 
6.8%
t2050
 
6.8%
h2050
 
6.8%
r2050
 
6.8%
F2025
 
6.7%
m2025
 
6.7%
M1975
 
6.5%

score
Real number (ℝ)

Missing 

Distinct5600
Distinct (%)99.1%
Missing400
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean49.748432
Minimum0.010237253
Maximum99.997387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:42.923879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.010237253
5-th percentile4.8790672
Q125.298396
median49.884004
Q374.367371
95-th percentile94.761054
Maximum99.997387
Range99.98715
Interquartile range (IQR)49.068976

Descriptive statistics

Standard deviation28.778787
Coefficient of variation (CV)0.5784863
Kurtosis-1.1882461
Mean49.748432
Median Absolute Deviation (MAD)24.530472
Skewness0.0019551551
Sum281078.64
Variance828.21857
MonotonicityNot monotonic
2026-02-06T10:29:43.091442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.9080171352
 
< 0.1%
11.34764022
 
< 0.1%
90.411081262
 
< 0.1%
44.617958482
 
< 0.1%
68.87396542
 
< 0.1%
13.891442322
 
< 0.1%
2.1947482392
 
< 0.1%
29.039092152
 
< 0.1%
57.454563952
 
< 0.1%
1.9987755572
 
< 0.1%
Other values (5590)5630
93.1%
(Missing)400
 
6.6%
ValueCountFrequency (%)
0.010237252581
< 0.1%
0.014576760731
< 0.1%
0.016549650111
< 0.1%
0.024802370851
< 0.1%
0.03908558471
< 0.1%
0.048214047851
< 0.1%
0.092387578341
< 0.1%
0.11523407951
< 0.1%
0.11681464241
< 0.1%
0.15572348261
< 0.1%
ValueCountFrequency (%)
99.997386761
< 0.1%
99.98123881
< 0.1%
99.980848121
< 0.1%
99.959139671
< 0.1%
99.949611241
< 0.1%
99.939587991
< 0.1%
99.917693051
< 0.1%
99.917541251
< 0.1%
99.916230031
< 0.1%
99.912688921
< 0.1%

last_promotion_years
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9342149
Minimum0
Maximum14
Zeros386
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:43.214942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.2800737
Coefficient of variation (CV)0.61723984
Kurtosis-1.1882968
Mean6.9342149
Median Absolute Deviation (MAD)4
Skewness0.030154497
Sum41952
Variance18.319031
MonotonicityNot monotonic
2026-02-06T10:29:43.348391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9447
 
7.4%
3434
 
7.2%
5418
 
6.9%
7418
 
6.9%
4417
 
6.9%
6409
 
6.8%
10405
 
6.7%
1404
 
6.7%
14404
 
6.7%
2402
 
6.6%
Other values (5)1892
31.3%
ValueCountFrequency (%)
0386
6.4%
1404
6.7%
2402
6.6%
3434
7.2%
4417
6.9%
5418
6.9%
6409
6.8%
7418
6.9%
8368
6.1%
9447
7.4%
ValueCountFrequency (%)
14404
6.7%
13365
6.0%
12383
6.3%
11390
6.4%
10405
6.7%
9447
7.4%
8368
6.1%
7418
6.9%
6409
6.8%
5418
6.9%

joining_year
Real number (ℝ)

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.1666
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:43.469249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2477821
Coefficient of variation (CV)0.0036019791
Kurtosis-1.2195267
Mean2012.1666
Median Absolute Deviation (MAD)6
Skewness-0.014045943
Sum12173608
Variance52.530345
MonotonicityNot monotonic
2026-02-06T10:29:43.585082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2022286
 
4.7%
2024261
 
4.3%
2018260
 
4.3%
2016258
 
4.3%
2023250
 
4.1%
2012248
 
4.1%
2009246
 
4.1%
2005245
 
4.0%
2007244
 
4.0%
2021242
 
4.0%
Other values (15)3510
58.0%
ValueCountFrequency (%)
2000220
3.6%
2001240
4.0%
2002240
4.0%
2003237
3.9%
2004242
4.0%
2005245
4.0%
2006240
4.0%
2007244
4.0%
2008238
3.9%
2009246
4.1%
ValueCountFrequency (%)
2024261
4.3%
2023250
4.1%
2022286
4.7%
2021242
4.0%
2020229
3.8%
2019233
3.9%
2018260
4.3%
2017232
3.8%
2016258
4.3%
2015221
3.7%

projects_completed
Real number (ℝ)

Zeros 

Distinct50
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.257686
Minimum0
Maximum49
Zeros117
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:43.757549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median24
Q337
95-th percentile47
Maximum49
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.44885
Coefficient of variation (CV)0.59564007
Kurtosis-1.198318
Mean24.257686
Median Absolute Deviation (MAD)13
Skewness0.01267122
Sum146759
Variance208.76926
MonotonicityNot monotonic
2026-02-06T10:29:43.953624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1150
 
2.5%
39147
 
2.4%
21142
 
2.3%
48141
 
2.3%
4137
 
2.3%
27137
 
2.3%
8133
 
2.2%
10132
 
2.2%
38131
 
2.2%
15130
 
2.1%
Other values (40)4670
77.2%
ValueCountFrequency (%)
0117
1.9%
1150
2.5%
2126
2.1%
3122
2.0%
4137
2.3%
5117
1.9%
6116
1.9%
792
1.5%
8133
2.2%
9128
2.1%
ValueCountFrequency (%)
49103
1.7%
48141
2.3%
47117
1.9%
46102
1.7%
45117
1.9%
44125
2.1%
43127
2.1%
42110
1.8%
41113
1.9%
40107
1.8%

overtime_hours
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.542479
Minimum0
Maximum29
Zeros182
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:44.121800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median15
Q322
95-th percentile28
Maximum29
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6629698
Coefficient of variation (CV)0.59570102
Kurtosis-1.2217517
Mean14.542479
Median Absolute Deviation (MAD)8
Skewness-0.0079298502
Sum87982
Variance75.047046
MonotonicityNot monotonic
2026-02-06T10:29:44.279823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
22222
 
3.7%
12220
 
3.6%
8219
 
3.6%
24218
 
3.6%
27216
 
3.6%
5216
 
3.6%
4215
 
3.6%
21214
 
3.5%
23213
 
3.5%
1209
 
3.5%
Other values (20)3888
64.3%
ValueCountFrequency (%)
0182
3.0%
1209
3.5%
2202
3.3%
3204
3.4%
4215
3.6%
5216
3.6%
6183
3.0%
7195
3.2%
8219
3.6%
9192
3.2%
ValueCountFrequency (%)
29204
3.4%
28185
3.1%
27216
3.6%
26193
3.2%
25194
3.2%
24218
3.6%
23213
3.5%
22222
3.7%
21214
3.5%
20208
3.4%

leaves_taken
Real number (ℝ)

Zeros 

Distinct40
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.633388
Minimum0
Maximum39
Zeros148
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:44.446608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median20
Q330
95-th percentile37
Maximum39
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.490167
Coefficient of variation (CV)0.58523609
Kurtosis-1.1857117
Mean19.633388
Median Absolute Deviation (MAD)10
Skewness-0.024018954
Sum118782
Variance132.02395
MonotonicityNot monotonic
2026-02-06T10:29:44.584866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
35174
 
2.9%
16174
 
2.9%
34168
 
2.8%
17168
 
2.8%
20167
 
2.8%
15165
 
2.7%
1164
 
2.7%
24163
 
2.7%
19159
 
2.6%
31159
 
2.6%
Other values (30)4389
72.5%
ValueCountFrequency (%)
0148
2.4%
1164
2.7%
2142
2.3%
3154
2.5%
4149
2.5%
5136
2.2%
6149
2.5%
7133
2.2%
8141
2.3%
9154
2.5%
ValueCountFrequency (%)
39137
2.3%
38148
2.4%
37150
2.5%
36149
2.5%
35174
2.9%
34168
2.8%
33144
2.4%
32159
2.6%
31159
2.6%
30146
2.4%

performance_rating
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
4
1232 
1
1221 
2
1220 
5
1202 
3
1175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6050
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
41232
20.4%
11221
20.2%
21220
20.2%
51202
19.9%
31175
19.4%

Length

2026-02-06T10:29:44.742411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-06T10:29:44.856992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
41232
20.4%
11221
20.2%
21220
20.2%
51202
19.9%
31175
19.4%

Most occurring characters

ValueCountFrequency (%)
41232
20.4%
11221
20.2%
21220
20.2%
51202
19.9%
31175
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)6050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41232
20.4%
11221
20.2%
21220
20.2%
51202
19.9%
31175
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41232
20.4%
11221
20.2%
21220
20.2%
51202
19.9%
31175
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41232
20.4%
11221
20.2%
21220
20.2%
51202
19.9%
31175
19.4%

work_life_balance
Real number (ℝ)

Distinct6000
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0069992
Minimum1.0001902
Maximum4.9995802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.4 KiB
2026-02-06T10:29:45.016135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0001902
5-th percentile1.2137725
Q12.0135981
median2.9917309
Q34.0213522
95-th percentile4.8113226
Maximum4.9995802
Range3.99939
Interquartile range (IQR)2.0077541

Descriptive statistics

Standard deviation1.1583247
Coefficient of variation (CV)0.38520952
Kurtosis-1.2204791
Mean3.0069992
Median Absolute Deviation (MAD)1.0034281
Skewness0.0040208619
Sum18192.345
Variance1.3417162
MonotonicityNot monotonic
2026-02-06T10:29:45.221487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6839490752
 
< 0.1%
4.9530607592
 
< 0.1%
1.8490685072
 
< 0.1%
2.3925570042
 
< 0.1%
2.9188674692
 
< 0.1%
1.6979869332
 
< 0.1%
4.2121812762
 
< 0.1%
2.5040442022
 
< 0.1%
4.5863491092
 
< 0.1%
3.6986265722
 
< 0.1%
Other values (5990)6030
99.7%
ValueCountFrequency (%)
1.0001901561
< 0.1%
1.0010790611
< 0.1%
1.0019558721
< 0.1%
1.0026696571
< 0.1%
1.004220371
< 0.1%
1.0044729271
< 0.1%
1.0060692881
< 0.1%
1.0062418181
< 0.1%
1.0065739411
< 0.1%
1.0067988881
< 0.1%
ValueCountFrequency (%)
4.9995801651
< 0.1%
4.9987261391
< 0.1%
4.9978945242
< 0.1%
4.9955163161
< 0.1%
4.9953442061
< 0.1%
4.9951868241
< 0.1%
4.9942636771
< 0.1%
4.9940896031
< 0.1%
4.9938692341
< 0.1%
4.9936669091
< 0.1%

travel_frequency
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.4 KiB
Rarely
2082 
Very Often
1987 
Often
1981 

Length

Max length10
Median length6
Mean length6.986281
Min length5

Characters and Unicode

Total characters42267
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Often
2nd rowOften
3rd rowRarely
4th rowRarely
5th rowRarely

Common Values

ValueCountFrequency (%)
Rarely2082
34.4%
Very Often1987
32.8%
Often1981
32.7%

Length

2026-02-06T10:29:45.404985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-06T10:29:45.519887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
often3968
49.4%
rarely2082
25.9%
very1987
24.7%

Most occurring characters

ValueCountFrequency (%)
e8037
19.0%
r4069
9.6%
y4069
9.6%
t3968
9.4%
n3968
9.4%
O3968
9.4%
f3968
9.4%
a2082
 
4.9%
R2082
 
4.9%
l2082
 
4.9%
Other values (2)3974
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)42267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8037
19.0%
r4069
9.6%
y4069
9.6%
t3968
9.4%
n3968
9.4%
O3968
9.4%
f3968
9.4%
a2082
 
4.9%
R2082
 
4.9%
l2082
 
4.9%
Other values (2)3974
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8037
19.0%
r4069
9.6%
y4069
9.6%
t3968
9.4%
n3968
9.4%
O3968
9.4%
f3968
9.4%
a2082
 
4.9%
R2082
 
4.9%
l2082
 
4.9%
Other values (2)3974
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8037
19.0%
r4069
9.6%
y4069
9.6%
t3968
9.4%
n3968
9.4%
O3968
9.4%
f3968
9.4%
a2082
 
4.9%
R2082
 
4.9%
l2082
 
4.9%
Other values (2)3974
9.4%

Interactions

2026-02-06T10:29:38.570799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:25.311117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:26.695881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.504654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.415737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.690426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.139182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.463586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.890582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.242575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:38.839615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:25.475274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:26.893746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.633876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.498671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.807660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.290712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.620847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.009378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.377229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:38.950069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:25.582397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:27.101600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.800395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.598233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.951134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.447649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.768446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.153104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.493545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.090438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:25.694174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:27.306102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.966381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.733070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:32.139933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.610808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.893394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.303162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.633250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.242403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:25.790988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:27.538674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:29.166381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.891200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:32.272226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.728201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.054628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.452677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.766679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.376221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:25.885040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:27.794865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:29.419724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.029660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:32.427479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.831823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.191302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.559466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.942053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.505619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:26.040656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:27.895631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:29.666579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.127850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:32.565843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.930901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.318737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.689103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:38.082613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.606683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:26.183420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.011092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:29.896116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.236853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:32.705463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.049400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.441387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.836773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:38.194092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.762162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:26.302039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.131090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.082131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.382195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:32.869660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.204609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.582547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:36.993216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:38.311581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:39.905344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:26.446206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:28.308458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:30.270941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:31.560240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:33.001978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:34.338988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:35.711345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:37.114008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-06T10:29:38.427398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-06T10:29:45.654782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
leaves_takenagecitydepartmenteducation_levelexperience_yearsgenderjoining_yearlast_promotion_yearsovertime_hoursperformance_ratingprojects_completedsalaryscoretravel_frequencywork_life_balance
leaves_taken1.0000.0170.0260.0130.000-0.0010.0140.0170.0180.0140.0010.016-0.018-0.0130.000-0.030
age0.0171.0000.0130.0240.0080.0210.000-0.011-0.0020.0080.018-0.0110.014-0.0120.027-0.015
city0.0260.0131.0000.0180.0000.0000.0160.0000.0020.0150.0150.0100.0000.0300.0000.005
department0.0130.0240.0181.0000.0150.0000.0210.0190.0000.0000.0090.0100.0040.0150.0050.013
education_level0.0000.0080.0000.0151.0000.0130.0000.0130.0000.0000.0200.0050.0000.0280.0160.005
experience_years-0.0010.0210.0000.0000.0131.0000.000-0.0080.009-0.0210.0000.0240.0090.0110.0000.013
gender0.0140.0000.0160.0210.0000.0001.0000.0060.0210.0190.0180.0000.0100.0080.0000.015
joining_year0.017-0.0110.0000.0190.013-0.0080.0061.0000.006-0.0060.0150.0380.032-0.0150.0000.020
last_promotion_years0.018-0.0020.0020.0000.0000.0090.0210.0061.0000.0030.017-0.0200.000-0.0200.009-0.002
overtime_hours0.0140.0080.0150.0000.000-0.0210.019-0.0060.0031.0000.0000.0060.007-0.0030.000-0.009
performance_rating0.0010.0180.0150.0090.0200.0000.0180.0150.0170.0001.0000.0160.0070.0110.0080.009
projects_completed0.016-0.0110.0100.0100.0050.0240.0000.038-0.0200.0060.0161.0000.0300.0460.0000.011
salary-0.0180.0140.0000.0040.0000.0090.0100.0320.0000.0070.0070.0301.000-0.0130.0000.013
score-0.013-0.0120.0300.0150.0280.0110.008-0.015-0.020-0.0030.0110.046-0.0131.0000.0180.003
travel_frequency0.0000.0270.0000.0050.0160.0000.0000.0000.0090.0000.0080.0000.0000.0181.0000.000
work_life_balance-0.030-0.0150.0050.0130.0050.0130.0150.020-0.002-0.0090.0090.0110.0130.0030.0001.000

Missing values

2026-02-06T10:29:40.159672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-06T10:29:40.375793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-06T10:29:40.560675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agesalaryexperience_yearseducation_leveldepartmentcitygenderscorelast_promotion_yearsjoining_yearprojects_completedovertime_hoursleaves_takenperformance_ratingwork_life_balancetravel_frequency
05663919.024.0High SchoolSalesBangaloreOther41.28593714202223221941.849069Very Often
169102103.026.0High SchoolMarketingDelhiOther8.90801711200119123742.392557Often
246142443.05.0PhDHRBangaloreMale11.3476405201236213542.918867Rarely
33223358.033.0BachelorFinanceDelhiOther90.4110811120213091141.697987Rarely
460NaN8.0PhDSalesBangaloreFemale44.617958920054617444.212181Rarely
525127894.032.0MasterSalesMumbaiMale68.87396542008161432.504044Often
638141485.05.0High SchoolSalesDelhiFemale13.89144214201348211644.586349Very Often
756NaN36.0MasterFinanceDelhiOther2.1947486202323113743.698627Rarely
836143416.030.0High SchoolHRBangaloreMale89.0483961020201712951.706424Very Often
940125982.033.0BachelorMarketingPuneFemale57.45456414200625241533.567885Very Often
agesalaryexperience_yearseducation_leveldepartmentcitygenderscorelast_promotion_yearsjoining_yearprojects_completedovertime_hoursleaves_takenperformance_ratingwork_life_balancetravel_frequency
604020118262.09.0High SchoolHRNaNFemale17.903324420208251354.155077Often
60415438782.022.0PhDFinanceHyderabadOther72.99332712200445113441.415999Often
60426898257.033.0MasterITHyderabadFemale88.365334020031123252.761524Very Often
604324NaN32.0MasterMarketingBangaloreFemale7.8786211320219192824.997895Often
60443897963.0NaNPhDSalesMumbaiOther78.503786320144561521.485468Rarely
604526132086.038.0MasterFinanceMumbaiMale89.57222942023432934.260105Very Often
604656141987.09.0BachelorITDelhiOther97.39702232009438314.196832Very Often
60473540566.019.0MasterITMumbaiFemale57.93862592004114342.780164Rarely
604821144591.0NaNBachelorFinanceDelhiMale32.739189920131814114.953061Rarely
60494287766.012.0High SchoolSalesDelhiMale32.197360620231271022.231873Very Often

Duplicate rows

Most frequently occurring

agesalaryexperience_yearseducation_leveldepartmentcitygenderscorelast_promotion_yearsjoining_yearprojects_completedovertime_hoursleaves_takenperformance_ratingwork_life_balancetravel_frequency# duplicates
01928002.011.0BachelorFinanceBangaloreFemale91.3697821020224353511.683949Often2
11995033.020.0High SchoolHRMumbaiOther29.69535702005485111.448118Often2
22096777.025.0High SchoolITNaNOther56.913837132021191622.694355Rarely2
320118262.09.0High SchoolHRNaNFemale17.903324420208251354.155077Often2
421144591.0NaNBachelorFinanceDelhiMale32.739189920131814114.953061Rarely2
524NaN32.0MasterMarketingBangaloreFemale7.8786211320219192824.997895Often2
625127894.032.0MasterSalesMumbaiMale68.87396542008161432.504044Often2
726132086.038.0MasterFinanceMumbaiMale89.57222942023432934.260105Very Often2
82825698.013.0High SchoolSalesMumbaiFemale99.233948142004662834.229508Often2
928117057.028.0MasterSalesPuneFemale1.9987763202241202321.092991Rarely2